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Neural Network Branch-and-Bound for Neural Network Verification
[article]
2021
arXiv
pre-print
Many available formal verification methods have been shown to be instances of a unified Branch-and-Bound (BaB) formulation. We propose a novel machine learning framework that can be used for designing an effective branching strategy as well as for computing better lower bounds. Specifically, we learn two graph neural networks (GNN) that both directly treat the network we want to verify as a graph input and perform forward-backward passes through the GNN layers. We use one GNN to simulate the
arXiv:2107.12855v1
fatcat:3hktdiw2vbfndbyec4c7yohvsm